Learning about Constraints

About learning constraints related matters.

Parameters

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One major challenge of learning the features of constraints is exploring the domains of parameters. To tackle this issue, we provide some way to generate parameters from variables' domains.

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Missing docstring for BoolParameterDomain. Check Documenter's build log for details.

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Missing docstring for DimParameterDomain. Check Documenter's build log for details.

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Missing docstring for IdParameterDomain. Check Documenter's build log for details.

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Missing docstring for FakeAutomaton. Check Documenter's build log for details.

# ConstraintCommons.accept — Function.

accept(a::Union{Automaton, MDD}, w)

Return true if a accepts the word w and false otherwise.

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ConstraintCommons.accept(fa::FakeAutomaton, word)

Implement the accept methods for FakeAutomaton.

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Missing docstring for fake_automaton. Check Documenter's build log for details.

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Missing docstring for LanguageParameterDomain. Check Documenter's build log for details.

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Missing docstring for OpParameterDomain. Check Documenter's build log for details.

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Missing docstring for PairVarsParameterDomain. Check Documenter's build log for details.

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Missing docstring for ValParameterDomain. Check Documenter's build log for details.

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Missing docstring for ValsParameterDomain. Check Documenter's build log for details.

# Base.rand — Function.

Base.rand(d::Union{Vector{D},Set{D}, D}) where {D<:AbstractDomain}

Extends Base.rand to (a collection of) domains.

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Base.rand(itv::Intervals)
Base.rand(itv::Intervals, i)

Return a random value from itv, specifically from the ith interval if i is specified.

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Base.rand(d::D) where D <: DiscreteDomain

Draw randomly a point in d.

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Base.rand(fa::FakeAutomaton)

Extends Base.rand. Currently simply returns fa.

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# ConstraintDomains.generate_parameters — Function.

generate_parameters(d<:AbstractDomain, param)

Generates random parameters based on the domain d and the kind of parameters param.

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